MAXIMUM-LIKELIHOOD JOINT CHANNEL AND DATA ESTIMATION USING GENETIC ALGORITHMS

A batch blind equalization scheme is developed based on maximum likeli hood joint channel and data estimation. In this scheme, the joint maxi mum likelihood optimization is decomposed into a two-level optimizatio n loop. A micro genetic algorithm is employed at the upper level to id entify the unknown channel model, and the Viterbi algorithm is used at the lower level to provide the maximum likelihood sequence estimation of the transmitted data sequence. As is demonstrated in simulation, t he proposed method is much more accurate compared with existing algori thms for joint channel and data estimation.